Evaluating Large Language Models for imaging modality selection: Potential to reduce unnecessary contrast agent use and radiation exposure
This study underscores that LLMs have remarkable potential for selecting appropriate imaging modalities for different clinical scenarios related to various sections and their valuable contributions as supportive tools in clinical practice in this field.
Authors
- İbrahim Ethem Cakcak (ORCID: https://orcid.org/0000-0001-8885-514X)
- Turay Cesur (ORCID: https://orcid.org/0000-0002-2726-8045)
- Yasin Celal Güneş (ORCID: https://orcid.org/0000-0001-7631-854X)
- Ersin Doğanözü (ORCID: https://orcid.org/0000-0002-0349-2691)
- Ahmet Kürşad Güneş (ORCID: https://orcid.org/0000-0001-5522-8342)
- Eren Çamur (ORCID: https://orcid.org/0000-0002-8774-5800)
- Yusuf Öztürk (ORCID: https://orcid.org/0009-0009-6372-1845)
- Yunus Şerefettin (ORCID: https://orcid.org/0009-0005-0987-9300)
- Ayşegül Akçebe (ORCID: https://orcid.org/0000-0002-5094-5948)
Institutions
- Trakya University (TR)
- Ministry of Health (TR)
- State Hospital (GB)
- Istinye University (TR)
- Gaziosmanpaşa Taksim Eğitim ve Araştırma Hastanesi (TR)
- Ankara Bilkent City Hospital (TR)
- Kırıkkale University (TR)
Publication Details
- Journal
- Clinical Imaging
- Published
- 2025-07-26
- DOI
- https://doi.org/10.1016/j.clinimag.2025.110573
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36